Meta Analysis and Research Synthesis • mars

mars provides tools for meta-analysis and research synthesis in R, with support for univariate, multivariate, and multilevel meta-analytic models. It also includes helpers for within-study variance-covariance matrices, path analysis after correlation synthesis, publication-bias models, diagnostics, network meta-analysis, reporting, and exploratory random-forest meta-analysis.

Installation

Install the released version from CRAN with:

install.packages("mars")

The package can be installed directly from GitHub using devtools or the remotes package with the following command:

remotes::install_github("lebebr01/mars")

To install the package vignettes, add the build_vignettes argument:

remotes::install_github("lebebr01/mars",
                        build_vignettes = TRUE)

Quick Example

A univariate random-effects meta-analysis can be fit with mars() by supplying an effect-size column, a sampling-variance column, and a study identifier:

library(mars)

fit <- mars(
  data = teacher_expectancy,
  studyID = "study",
  effectID = NULL,
  sample_size = NULL,
  formula = yi ~ 1,
  variance = "vi",
  varcov_type = "univariate",
  structure = "univariate"
)

summary(fit)

Choosing a workflow

Most analyses start with mars(), but the package also includes higher-level helpers for common research-synthesis tasks:

Goal	Start with	Learn more
Estimate an average effect, meta-regression, or dependent-effects model	`mars()`	Installed vignette: `vignette("MARS-Model-Examples", package = "mars")`
Reproduce the Becker, Aloe, and Cheung correlation-synthesis example	`mars()`, `df_to_corr()`, `path_model()`	Installed vignette: `vignette("Becker-Aloe-Cheung-2019", package = "mars")`
Fix heterogeneity values for sensitivity analyses	`mars(tau2 = ...)`	Installed vignette: `vignette("User-Specified-Heterogeneity", package = "mars")`
Choose among core workflows	`mars()`	pkgdown article: Choosing a MARS workflow
Build within-study variance-covariance matrices	`mars()` with `varcov_type`	pkgdown article: Within-study variance-covariance metrics
Diagnose residuals, influence, or heterogeneity	`residual_diagnostics()`, `influence_diagnostics()`, `profile_random_effects()`	pkgdown article: Residual and influence diagnostics
Assess publication bias or sensitivity	`publication_bias()`, `mars_alt_estimation()`	pkgdown articles for publication bias and alternative estimation
Fit treatment networks	`network_meta()`	pkgdown article: Network meta-analysis
Fit path, EFA, or CFA models after correlation synthesis	`path_model()`, `efa_from_synthesis()`, `cfa_from_synthesis()`	pkgdown articles for latent-factor and synthesis-report workflows
Create review and reporting graphics	`prisma_diagram()`, `risk_of_bias_plot()`, `gap_map_plot()`	pkgdown articles for review graphics
Explore nonlinear moderator patterns	`mars_rf()`	pkgdown article: Random-forest meta-analysis

Explore articles

The CRAN package installs a small set of core vignettes. Additional workflow articles are built for the pkgdown site at https://mars.brandonlebeau.org/.

Available installed vignettes can be accessed with the following code:

vignette(package = 'mars')